Hiring: Member of Technical Staff - AI & Physics Simulations Company: Collinear AI Location: San Francisco Bay Area Job Posted Time: 2026-09-14 13:04:11 Target Skills & Keywords : CI/CD, Deep Learning, LLM, Linux, Machine Learning, Project Management, Python, RAG, Unit Testing About the job Required Skills: •Design and orchestrate large-scale simulation campaigns using domain-specific solvers (e.g., OpenFOAM, ANSYS, COMSOL, Abaqus). •Train AI models on physics datasets and conduct rigorous evaluations of coverage, accuracy, and output quality against industrial validation standards. •Develop robust automated frameworks for dataset creation, simulation pipeline orchestration, and continuous model evaluation. •Architect agentic workflows and Retrieval-Augmented Generation (RAG) systems that seamlessly connect LLMs with engineering simulation pipelines. •Partner closely with the research team to analyze training runs, diagnose failure modes, and address data sparsity or architecture bottlenecks. •Lead research initiatives and manage technical communications with external engineering teams. Qualifications: •Ph.D. or Master's degree in Machine Learning, Mechanical Engineering, Electrical Engineering, Computational Physics, Structural Mechanics, Semiconductor Engineering, or a related field. •Solid grounding in deep learning principles paired with a strong foundation in physics or engineering sciences. •Hands-on experience implementing and training deep learning models. •Demonstrated ability to write clean, maintainable Python in Linux and High-Performance Computing (HPC) environments. •Outstanding verbal and written communication skills, with the ability to explain complex technical concepts to both specialized engineers and non-technical stakeholders. •Self-directed operator who thrives with autonomy, maintains a low-ego approach to collaboration, and excels in fast-paced environments at the intersection of simulation and ML. •Hands-on industrial or academic experience with simulation solvers (e.g., OpenFOAM, ANSYS, COMSOL, Abaqus). •Direct experience applying machine learning to physics simulations or surrogate modeling (e.g., Neural Operators, Physics-Informed Neural Networks). •Track record of automating large-scale simulation workloads on HPC clusters. •Meaningful contributions to large-scale open-source projects or production codebases. Compensation: •$150,000 - $400,000 / year Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!